基于强化学习的多机器人合作行为获取

研究了基于强化学习的多机器人学习社会合作行为的问题。通过定义的分配布尔矩阵，对参与任务者进行奖励回报，并综合基础行为，生成状态到行为的新的映射，形成高级的群体合作行为，使得团队作为一个整体受益。讨论了学习社会行为的可行性和必要性，并采用强化学习方法，给出了多机器人传接合作搬运的详细算法实现。

Multi-Robot Cooperative Behavior Generation Based on Reinforcement Learning

Learning social cooperative behavior in multi-robot was introduced, which assigns reinforcement by defining a boolean matrix, to agents if they have ever participated the accomplished task, and synthesizes basic behaviors to generate a new mapping from states to behaviors, and forms higher-level group cooperation, which will benefit the group as a whole. The possibility and necessity of learning social behavior were discussed, and applying reinforcement learning and the above idea to multi-agent's learning relay cooperation in convey.